One of the major capabilities of humans over machines is the ability to work with less than perfect information. Machines can process exact inputs and deliver exact answers—the exception is often due to a defect which may not the intent or design. This capability is also referred to as satisficing, a “handy blended word combining satisfy with suffice”, is a decision-making strategy that attempts to meet criteria for adequacy, rather than to identify an optimal solution—in short, satisficing is to identify a “good enough” solution.
Machines can be built to optimize using probabilities of outcomes, evaluating outcomes with sufficient precision, storing and retrieving even very large sets of outcomes and crunching them rapidly with sophisticated computing techniques. Humans lack many of these capabilities and yet excel at finding a good-enough answer to most decisions almost instinctively or instantaneously. Many agree that satisficing skill is applied in almost all human actions or decisions. Put a tennis ball into someone's hand and ask how much it weighs. The answer could be instantaneous as 4 ounces or 5 ounces or some other guess. The focus in not on an exact answer or an optimal answer. The focus is on a good-enough answer with a good-enough precision.
In the simple decision discussed above, at least two references are made to the application of satisficing. Humans make many thousands of decisions on an average day such as how much force to apply to lift something, how hard to throw a ball over the fence, what's the temperature now, etc. Based on this ubiquity of the application of satisficing capability, many agree that this capability cannot be an optimization machine, must be driven by simple math, must be evaluated with minimal computing needs, cannot burden the actual process of decision making, must be scalable in that it must have similar response times irrespective of number of ordered elements, must be universally applicable whether it find a good-enough height, good-enough precision, good-enough market capital of a company, etc.
This capability remains one of the most critical gaps or challenges in any machine application that tries to emulate any of the human capabilities. The applications cover a broad spectrum such as speech recognition, image recognition, optical character recognition, smell sensors, pressure sensors, gesture recognition, digital content recognition, language processing, semantic search, motion detection, sound detectors, etc.